Detection Recovery in Online Multi-Object Tracking with Sparse Graph Tracker

In existing joint detection and tracking methods, pairwise relational features are used to match previous tracklets to current detections. However, the features may not be discriminative enough for a tracker to identify a target from a large number of detections. Selecting only high-scored detections for tracking may lead to missed detections whose confidence score is low. Consequently, in the online setting, this results in disconnections of tracklets which cannot be recovered. In this regard, we present Sparse Graph Tracker (SGT), a novel online graph tracker using higher-order relational features which are more discriminative by aggregating the features of neighboring detections and their relations. SGT converts video data into a graph where detections, their connections, and the relational features of two connected nodes are represented by nodes, edges, and edge features, respectively. The strong edge features allow SGT to track targets with tracking candidates selected by top-K scored detections with large K. As a result, even low-scored detections can be tracked, and the missed detections are also recovered. The robustness of K value is shown through the extensive experiments. In the MOT16/17/20 and HiEve Challenge, SGT outperforms the state-of-the-art trackers with real-time inference speed. Especially, a large improvement in MOTA is shown in the MOT20 and HiEve Challenge. Code is available at https://github.com/HYUNJS/SGT.Comment: Accepted to WACV 2023; fix figure

Smart City As a Social Transition Towards Inclusive Development Through Technology: a Tale of Four Smart Cities

Smart city initiatives have the potential to address many contemporary urban challenges, utilizing information and technology. Increasingly, smart cities are considered as social innovation processes to achieve sustainable and inclusive urban development, being influenced by broader socio-economic and institutional contexts of cities. This paper explores ‘smart city transitions’ across varied urban contexts, in particular, how smart city transitions are enacted and how they contribute to inclusive urban transformation and public value. Using a multiple case studies approach, the research investigated infrastructure planning practices in Amsterdam in the Netherlands, Seoul in Korea, Portland in the U.S. and Ho Chi Minh City (HCMC) in Vietnam, cities that were known for strong efforts to establish integrated platforms to enhance societal benefits. Our analysis showed that each city has addressed its goals around sustainability, equity and affordability by reinforcing the engagement of multiple actors with the support of integrated platforms that facilitate open and multi-directional information flow in a transparent manner. In Amsterdam, innovative solutions for sustainable use of resources have been invented and distributed through multi-level social networks, contributing to the transformation into a circular economy. In Seoul and HCMC, the city\u27s persistent efforts to utilize an open and integrated platform resulted in proactive engagement and collaboration of public and private actors in improving quality, equity and efficiency of transit services. Portland has tackled inequitable access and mistrust issues by setting principles for data governance and facilitating equity in the adoption of innovative technologies. Our research revealed that four cities established different forms of integrated platforms such as a centrally-controlled platform and a community-centred platform in order to address specific socio-economic issues within an institutional setting of each city. We concluded that building an integrated platform is not easy, but it is a critical prerequisite for the process of sustainable transformation to truly achieve smart cities across the globe

Investigating the physical characteristics and cellular interplay on 3D-printed scaffolds depending on the incorporated silica size for hard tissue regeneration

Silica has been widely used in bone tissue regeneration which is known to increase the bone mineral density and reduce bone resorption. In this study, surface modified silica particles with different sizes (100, 500, and 800 nm) were incorporated with polycaprolactone (PCL) to study the influence of silica particle size on physical and biological properties. Controversial results were observed between the physical and biological properties. In terms of physical properties including surface roughness, hydrophilicity, and mechanical strength, the PCL scaffold with 800 nm-sized particles showed significantly enhanced results. However, the scaffold with 100 nm-sized particles significantly upregulated the biological properties such as human mesenchymal stem cell adhesion, proliferation, and differentiation. This was also relevant for the in vivo results. Altogether, the results proved that the silica particle size influence the physical and biological properties of the PCL scaffold

The Sixth Visual Object Tracking VOT2018 Challenge Results

The Visual Object Tracking challenge VOT2018 is the sixth annual tracker benchmarking activity organized by the VOT initiative. Results of over eighty trackers are presented; many are state-of-the-art trackers published at major computer vision conferences or in journals in the recent years. The evaluation included the standard VOT and other popular methodologies for short-term tracking analysis and a “real-time” experiment simulating a situation where a tracker processes images as if provided by a continuously running sensor. A long-term tracking subchallenge has been introduced to the set of standard VOT sub-challenges. The new subchallenge focuses on long-term tracking properties, namely coping with target disappearance and reappearance. A new dataset has been compiled and a performance evaluation methodology that focuses on long-term tracking capabilities has been adopted. The VOT toolkit has been updated to support both standard short-term and the new long-term tracking subchallenges. Performance of the tested trackers typically by far exceeds standard baselines. The source code for most of the trackers is publicly available from the VOT page. The dataset, the evaluation kit and the results are publicly available at the challenge website (http://votchallenge.net).Funding agencies: Slovenian research agencySlovenian Research Agency - Slovenia [P2-0214, P2-0094, J2-8175]; Czech Science FoundationGrant Agency of the Czech Republic [GACR P103/12/G084]; WASP; VR (EMC2); SSF (SymbiCloud); SNIC; AIT Strategic Research Programme 2017 Visua</p